Forecasting OPEC oil price: a comparison of parametric stochastic models

Most academic papers on oil price forecasting have frequently focused on the use of WTI and European Brent oil price series with little focus on other equally important international oil price benchmarks such as the OPEC Reference Basket (ORB). The ORB is a weighted average of 11-member countries crude streams weighted according to production and exports to the main markets. This paper compares the forecasting accuracy of four stochastic processes and four univariate random walk models using daily data of OPEC Reference Basket series. The study finds that the random walk univariate model outperforms the other stochastic processes. An element of uncertainty was introduced into the point estimates by deriving probability distribution that describes the possible price paths on a given day and their likelihood of occurrence. This will help decision makers, traders and analysts to have a better understanding of the possible daily prices that could occur. JEL Classification Numbers: E64; C22; Q30 Keywords: Oil Price Forecasting, Probability Distributions, and Forecast Evaluation Statistics, Brownian Motion with Mean Reversion process, GARCH Model

Time series analysis for minority game simulations of financial markets

The minority game (MG) model introduced recently provides promising insights into the understanding of the evolution of prices, indices and rates in the financial markets. In this paper we perform a time series analysis of the model employing tools from statistics, dynamical systems theory and stochastic processes. Using benchmark systems and a financial index for comparison, several conclusions are obtained about the generating mechanism for this kind of evolut ion. The motion is deterministic, driven by occasional random external perturbation. When the interval between two successive perturbations is sufficiently large, one can find low dimensional chaos in this regime. However, the full motion of the MG model is found to be similar to that of the first differences of the SP500 index: stochastic, nonlinear and (unit root) stationary.Comment: LaTeX 2e (elsart), 17 pages, 3 EPS figures and 2 tables, accepted for publication in Physica

Forecasting Irish inflation using ARIMA models

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models - the Box Jenkins approach and the objective penalty function methods. The emphasis is on forecast performance which suggests more focus on minimising out-of-sample forecast errors than on maximising in-sample 'goodness of fit'. Thus, the approach followed is unashamedly one of 'model mining' with the aim of optimising forecast performance. Practical issues in ARIMA time series forecasting are illustrated with reference to the harmonised index of consumer prices (HICP) and some of its major sub-components.

Inconsistencies in the application of harmonic analysis to pulsating stars

Using ultra-precise data from space instrumentation we found that the underlying functions of stellar light curves from some AF pul- sating stars are non-analytic, and consequently their Fourier expansion is not guaranteed. This result demonstrates that periodograms do not provide a mathematically consistent estimator of the frequency content for this kind of variable stars. More importantly, this constitutes the first counterexample against the current paradigm which considers that any physical process is described by a contin- uous (band-limited) function that is infinitely differentiable.Comment: 9 pages, 8 figure

Time series models of GDP: a reappraisal.

We propose a model diagnostic device to compare different linear and non linear parametric time series models of real GDP business cycle.The comparison appears of remarkable economic importance since different models have very different implications in term of long run persistence of negative shocks on the level of aggregate output.On the basis of the proposed diagnostic six popular models of real GDP are compared in a Monte Carlo simulation.We find that SETAR models and three stages Markov-switching models significantlly overperform the other statistical representation of the series.Since the SETAR form of non linearity is far easier to handle for both estimation and testing we argue in their favour.SETAR models, ARMA models, Markov-switching models, impulse response functions, residual based misspecification tests,busyness-cycle stylized facts

Managing Uncertainty: A Case for Probabilistic Grid Scheduling

The Grid technology is evolving into a global, service-orientated architecture, a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of elivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job historical executions and its associated meta-data. This approach is motivated by a study of six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture

Analysing correlation between the MSE index and global stock markets

The paper investigates the time-varying correlation between the Malta Stock Exchange (MSE) index, and five major international stock markets. An MGARCH-DCC approach is employed to measure the degree to which the MSE moves with other stock markets. Daily returns on these six stock exchange indices were computed and used to calculate dynamic conditional correlations (DCCs) between the markets. The results indicate that the local stock market appears not to be driven by the same forces that shape foreign stock markets, implying that local dynamics shape returns on the Exchange, rather than foreign events.peer-reviewe

The brittleness index in hydraulic fracturing

We present a new definition of a brittleness index which is used as a criterion for candidate selection of rock intervals for hydraulic fracturing. The new index is a combination of material strength parameters and insitu stresses. It was derived from an analytical model of hydraulic fracturing in weak formations of varying ductility. The model is based on Mohr-Coulomb dislocations that are placed in the effective centres of the complete slip process that is distributed around the crack tip. The new brittleness index varies between 0 and 1 with the one limit to correspond to brittle propagation and the other limit to a fracture that requires infinite energy release per unit advance. The values between 0 and 1 correspond to fracture propagation of increasing ductility from brittle to small scale and finally to large scale yielding. The results are particularly interesting for predicting the propagation of axial fractures in the horizontal direction and their confinement in the vertical direction